Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations5000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory426.5 B

Variable types

Text1
Categorical1
Numeric15
Boolean3

Alerts

day.charge is highly overall correlated with day.minsHigh correlation
day.mins is highly overall correlated with day.chargeHigh correlation
eve.charge is highly overall correlated with eve.minsHigh correlation
eve.mins is highly overall correlated with eve.chargeHigh correlation
intl.charge is highly overall correlated with intl.minsHigh correlation
intl.mins is highly overall correlated with intl.chargeHigh correlation
night.charge is highly overall correlated with night.minsHigh correlation
night.mins is highly overall correlated with night.chargeHigh correlation
voice.messages is highly overall correlated with voice.planHigh correlation
voice.plan is highly overall correlated with voice.messagesHigh correlation
intl.plan is highly imbalanced (54.8%)Imbalance
voice.messages has 3678 (73.6%) zerosZeros
customer.calls has 1023 (20.5%) zerosZeros

Reproduction

Analysis started2025-10-12 19:24:26.820291
Analysis finished2025-10-12 19:24:59.744305
Duration32.92 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

state
Text

Distinct51
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size288.2 KiB
2025-10-13T00:54:59.900010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKS
2nd rowOH
3rd rowNJ
4th rowOH
5th rowOK
ValueCountFrequency (%)
wv158
 
3.2%
mn125
 
2.5%
al124
 
2.5%
id119
 
2.4%
va118
 
2.4%
oh116
 
2.3%
tx116
 
2.3%
wy115
 
2.3%
ny114
 
2.3%
or114
 
2.3%
Other values (41)3781
75.6%
2025-10-13T00:55:00.602751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N1081
 
10.8%
A1059
 
10.6%
M918
 
9.2%
I768
 
7.7%
T616
 
6.2%
D576
 
5.8%
C517
 
5.2%
O509
 
5.1%
W477
 
4.8%
V467
 
4.7%
Other values (14)3012
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N1081
 
10.8%
A1059
 
10.6%
M918
 
9.2%
I768
 
7.7%
T616
 
6.2%
D576
 
5.8%
C517
 
5.2%
O509
 
5.1%
W477
 
4.8%
V467
 
4.7%
Other values (14)3012
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N1081
 
10.8%
A1059
 
10.6%
M918
 
9.2%
I768
 
7.7%
T616
 
6.2%
D576
 
5.8%
C517
 
5.2%
O509
 
5.1%
W477
 
4.8%
V467
 
4.7%
Other values (14)3012
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N1081
 
10.8%
A1059
 
10.6%
M918
 
9.2%
I768
 
7.7%
T616
 
6.2%
D576
 
5.8%
C517
 
5.2%
O509
 
5.1%
W477
 
4.8%
V467
 
4.7%
Other values (14)3012
30.1%

area.code
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size341.9 KiB
area_code_415
2495 
area_code_408
1259 
area_code_510
1246 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters65000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowarea_code_415
2nd rowarea_code_415
3rd rowarea_code_415
4th rowarea_code_408
5th rowarea_code_415

Common Values

ValueCountFrequency (%)
area_code_4152495
49.9%
area_code_4081259
25.2%
area_code_5101246
24.9%

Length

2025-10-13T00:55:00.707654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-13T00:55:00.783632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
area_code_4152495
49.9%
area_code_4081259
25.2%
area_code_5101246
24.9%

Most occurring characters

ValueCountFrequency (%)
a10000
15.4%
e10000
15.4%
_10000
15.4%
r5000
7.7%
c5000
7.7%
o5000
7.7%
d5000
7.7%
43754
 
5.8%
13741
 
5.8%
53741
 
5.8%
Other values (2)3764
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)65000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a10000
15.4%
e10000
15.4%
_10000
15.4%
r5000
7.7%
c5000
7.7%
o5000
7.7%
d5000
7.7%
43754
 
5.8%
13741
 
5.8%
53741
 
5.8%
Other values (2)3764
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)65000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a10000
15.4%
e10000
15.4%
_10000
15.4%
r5000
7.7%
c5000
7.7%
o5000
7.7%
d5000
7.7%
43754
 
5.8%
13741
 
5.8%
53741
 
5.8%
Other values (2)3764
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)65000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a10000
15.4%
e10000
15.4%
_10000
15.4%
r5000
7.7%
c5000
7.7%
o5000
7.7%
d5000
7.7%
43754
 
5.8%
13741
 
5.8%
53741
 
5.8%
Other values (2)3764
 
5.8%

account.length
Real number (ℝ)

Distinct218
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.2586
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:00.902467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q173
median100
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.69456
Coefficient of variation (CV)0.39592174
Kurtosis-0.10162108
Mean100.2586
Median Absolute Deviation (MAD)27
Skewness0.10929112
Sum501293
Variance1575.6581
MonotonicityNot monotonic
2025-10-13T00:55:01.049901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9065
 
1.3%
8759
 
1.2%
10557
 
1.1%
9357
 
1.1%
11256
 
1.1%
10155
 
1.1%
10055
 
1.1%
8655
 
1.1%
11654
 
1.1%
10354
 
1.1%
Other values (208)4433
88.7%
ValueCountFrequency (%)
111
0.2%
22
 
< 0.1%
38
0.2%
43
 
0.1%
52
 
< 0.1%
62
 
< 0.1%
75
0.1%
82
 
< 0.1%
93
 
0.1%
103
 
0.1%
ValueCountFrequency (%)
2431
 
< 0.1%
2381
 
< 0.1%
2331
 
< 0.1%
2322
< 0.1%
2252
< 0.1%
2242
< 0.1%
2222
< 0.1%
2211
 
< 0.1%
2173
0.1%
2161
 
< 0.1%

voice.plan
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
3677 
True
1323 
ValueCountFrequency (%)
False3677
73.5%
True1323
 
26.5%
2025-10-13T00:55:01.140528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

voice.messages
Real number (ℝ)

High correlation  Zeros 

Distinct48
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7552
Minimum0
Maximum52
Zeros3678
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:01.244410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317
95-th percentile37
Maximum52
Range52
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.546393
Coefficient of variation (CV)1.7467497
Kurtosis0.19912718
Mean7.7552
Median Absolute Deviation (MAD)0
Skewness1.3504932
Sum38776
Variance183.50477
MonotonicityNot monotonic
2025-10-13T00:55:01.397273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
03678
73.6%
3183
 
1.7%
2867
 
1.3%
2967
 
1.3%
3366
 
1.3%
2464
 
1.3%
2764
 
1.3%
3058
 
1.2%
2658
 
1.2%
3257
 
1.1%
Other values (38)738
 
14.8%
ValueCountFrequency (%)
03678
73.6%
41
 
< 0.1%
62
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
104
 
0.1%
112
 
< 0.1%
1211
 
0.2%
134
 
0.1%
149
 
0.2%
ValueCountFrequency (%)
521
 
< 0.1%
511
 
< 0.1%
502
 
< 0.1%
493
 
0.1%
485
 
0.1%
474
 
0.1%
468
0.2%
4511
0.2%
449
0.2%
4316
0.3%

intl.plan
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4527 
True
473 
ValueCountFrequency (%)
False4527
90.5%
True473
 
9.5%
2025-10-13T00:55:01.492895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

intl.mins
Real number (ℝ)

High correlation 

Distinct170
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.26178
Minimum0
Maximum20
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:01.600451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.7613957
Coefficient of variation (CV)0.2690952
Kurtosis0.65531661
Mean10.26178
Median Absolute Deviation (MAD)1.8
Skewness-0.20996629
Sum51308.9
Variance7.6253063
MonotonicityNot monotonic
2025-10-13T00:55:01.752741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.190
 
1.8%
9.888
 
1.8%
11.383
 
1.7%
11.481
 
1.6%
10.181
 
1.6%
10.980
 
1.6%
9.779
 
1.6%
10.678
 
1.6%
1178
 
1.6%
10.578
 
1.6%
Other values (160)4184
83.7%
ValueCountFrequency (%)
024
0.5%
0.41
 
< 0.1%
1.12
 
< 0.1%
1.31
 
< 0.1%
23
 
0.1%
2.12
 
< 0.1%
2.22
 
< 0.1%
2.41
 
< 0.1%
2.51
 
< 0.1%
2.61
 
< 0.1%
ValueCountFrequency (%)
201
< 0.1%
19.72
< 0.1%
19.31
< 0.1%
19.21
< 0.1%
18.92
< 0.1%
18.71
< 0.1%
18.51
< 0.1%
18.41
< 0.1%
18.31
< 0.1%
18.22
< 0.1%

intl.calls
Real number (ℝ)

Distinct21
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4352
Minimum0
Maximum20
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:01.877936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4567882
Coefficient of variation (CV)0.55392951
Kurtosis3.2681836
Mean4.4352
Median Absolute Deviation (MAD)1
Skewness1.3606925
Sum22176
Variance6.0358081
MonotonicityNot monotonic
2025-10-13T00:55:01.992841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3992
19.8%
4953
19.1%
2743
14.9%
5706
14.1%
6495
9.9%
7308
 
6.2%
1265
 
5.3%
8172
 
3.4%
9148
 
3.0%
1076
 
1.5%
Other values (11)142
 
2.8%
ValueCountFrequency (%)
024
 
0.5%
1265
 
5.3%
2743
14.9%
3992
19.8%
4953
19.1%
5706
14.1%
6495
9.9%
7308
 
6.2%
8172
 
3.4%
9148
 
3.0%
ValueCountFrequency (%)
201
 
< 0.1%
192
 
< 0.1%
184
 
0.1%
172
 
< 0.1%
167
 
0.1%
159
 
0.2%
146
 
0.1%
1319
0.4%
1223
0.5%
1145
0.9%

intl.charge
Real number (ℝ)

High correlation 

Distinct170
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.771196
Minimum0
Maximum5.4
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:02.132896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.24
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.74551371
Coefficient of variation (CV)0.26902237
Kurtosis0.65598855
Mean2.771196
Median Absolute Deviation (MAD)0.48
Skewness-0.21028611
Sum13855.98
Variance0.55579069
MonotonicityNot monotonic
2025-10-13T00:55:02.280818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
390
 
1.8%
2.6588
 
1.8%
3.0583
 
1.7%
3.0881
 
1.6%
2.7381
 
1.6%
2.9480
 
1.6%
2.6279
 
1.6%
2.8678
 
1.6%
2.9778
 
1.6%
2.8478
 
1.6%
Other values (160)4184
83.7%
ValueCountFrequency (%)
024
0.5%
0.111
 
< 0.1%
0.32
 
< 0.1%
0.351
 
< 0.1%
0.543
 
0.1%
0.572
 
< 0.1%
0.592
 
< 0.1%
0.651
 
< 0.1%
0.681
 
< 0.1%
0.71
 
< 0.1%
ValueCountFrequency (%)
5.41
< 0.1%
5.322
< 0.1%
5.211
< 0.1%
5.181
< 0.1%
5.12
< 0.1%
5.051
< 0.1%
51
< 0.1%
4.971
< 0.1%
4.941
< 0.1%
4.912
< 0.1%

day.mins
Real number (ℝ)

High correlation 

Distinct1961
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.2889
Minimum0
Maximum351.5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:02.423729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile91.7
Q1143.7
median180.1
Q3216.2
95-th percentile271.105
Maximum351.5
Range351.5
Interquartile range (IQR)72.5

Descriptive statistics

Standard deviation53.894699
Coefficient of variation (CV)0.2989352
Kurtosis-0.021294471
Mean180.2889
Median Absolute Deviation (MAD)36.3
Skewness-0.011730827
Sum901444.5
Variance2904.6386
MonotonicityNot monotonic
2025-10-13T00:55:02.579651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189.310
 
0.2%
15410
 
0.2%
159.59
 
0.2%
1809
 
0.2%
184.59
 
0.2%
174.59
 
0.2%
177.19
 
0.2%
183.48
 
0.2%
189.88
 
0.2%
215.68
 
0.2%
Other values (1951)4911
98.2%
ValueCountFrequency (%)
02
< 0.1%
2.61
< 0.1%
6.61
< 0.1%
7.21
< 0.1%
7.81
< 0.1%
7.91
< 0.1%
12.51
< 0.1%
17.61
< 0.1%
18.91
< 0.1%
19.51
< 0.1%
ValueCountFrequency (%)
351.51
< 0.1%
350.81
< 0.1%
346.81
< 0.1%
345.31
< 0.1%
338.41
< 0.1%
337.41
< 0.1%
335.51
< 0.1%
334.31
< 0.1%
332.91
< 0.1%
332.11
< 0.1%

day.calls
Real number (ℝ)

Distinct123
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.0294
Minimum0
Maximum165
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:02.729478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.831197
Coefficient of variation (CV)0.19825369
Kurtosis0.17856779
Mean100.0294
Median Absolute Deviation (MAD)13
Skewness-0.084890964
Sum500147
Variance393.27639
MonotonicityNot monotonic
2025-10-13T00:55:02.878784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105117
 
2.3%
102113
 
2.3%
95108
 
2.2%
94104
 
2.1%
97104
 
2.1%
100102
 
2.0%
110101
 
2.0%
112101
 
2.0%
92100
 
2.0%
108100
 
2.0%
Other values (113)3950
79.0%
ValueCountFrequency (%)
02
< 0.1%
301
 
< 0.1%
341
 
< 0.1%
351
 
< 0.1%
361
 
< 0.1%
392
< 0.1%
402
< 0.1%
422
< 0.1%
444
0.1%
453
0.1%
ValueCountFrequency (%)
1651
 
< 0.1%
1631
 
< 0.1%
1602
 
< 0.1%
1583
0.1%
1572
 
< 0.1%
1563
0.1%
1522
 
< 0.1%
1517
0.1%
1506
0.1%
1492
 
< 0.1%

day.charge
Real number (ℝ)

High correlation 

Distinct1961
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.610586
Minimum0
Maximum59.76
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:03.020973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.37
Q124.425
median30.6
Q336.75
95-th percentile46.0905
Maximum59.76
Range59.76
Interquartile range (IQR)12.325

Descriptive statistics

Standard deviation9.2313776
Coefficient of variation (CV)0.30157468
Kurtosis0.059400635
Mean30.610586
Median Absolute Deviation (MAD)6.17
Skewness-0.049651216
Sum153052.93
Variance85.218333
MonotonicityNot monotonic
2025-10-13T00:55:03.170230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.1810
 
0.2%
26.1810
 
0.2%
29.679
 
0.2%
27.129
 
0.2%
30.119
 
0.2%
09
 
0.2%
31.379
 
0.2%
30.69
 
0.2%
28.638
 
0.2%
24.438
 
0.2%
Other values (1951)4910
98.2%
ValueCountFrequency (%)
09
0.2%
0.441
 
< 0.1%
1.121
 
< 0.1%
1.221
 
< 0.1%
1.331
 
< 0.1%
1.341
 
< 0.1%
2.131
 
< 0.1%
2.991
 
< 0.1%
3.211
 
< 0.1%
3.321
 
< 0.1%
ValueCountFrequency (%)
59.761
< 0.1%
59.641
< 0.1%
58.961
< 0.1%
58.71
< 0.1%
57.531
< 0.1%
57.361
< 0.1%
57.041
< 0.1%
56.831
< 0.1%
56.591
< 0.1%
56.461
< 0.1%

eve.mins
Real number (ℝ)

High correlation 

Distinct1876
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.61754
Minimum0
Maximum363.7
Zeros25
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:03.313165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile115.9
Q1165.7
median200.8
Q3233.9
95-th percentile283.305
Maximum363.7
Range363.7
Interquartile range (IQR)68.2

Descriptive statistics

Standard deviation52.304192
Coefficient of variation (CV)0.26202202
Kurtosis0.67122543
Mean199.61754
Median Absolute Deviation (MAD)34.2
Skewness-0.22704121
Sum998087.7
Variance2735.7285
MonotonicityNot monotonic
2025-10-13T00:55:03.470640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
025
 
0.5%
230.910
 
0.2%
199.710
 
0.2%
169.910
 
0.2%
167.69
 
0.2%
216.59
 
0.2%
1949
 
0.2%
1879
 
0.2%
161.79
 
0.2%
223.59
 
0.2%
Other values (1866)4891
97.8%
ValueCountFrequency (%)
025
0.5%
22.31
 
< 0.1%
31.21
 
< 0.1%
37.81
 
< 0.1%
41.71
 
< 0.1%
42.21
 
< 0.1%
42.51
 
< 0.1%
43.91
 
< 0.1%
47.32
 
< 0.1%
48.11
 
< 0.1%
ValueCountFrequency (%)
363.71
< 0.1%
361.81
< 0.1%
359.31
< 0.1%
354.21
< 0.1%
352.11
< 0.1%
351.61
< 0.1%
350.91
< 0.1%
350.51
< 0.1%
349.41
< 0.1%
348.91
< 0.1%

eve.calls
Real number (ℝ)

Distinct126
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.191
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:03.619300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.826496
Coefficient of variation (CV)0.19788699
Kurtosis0.1173634
Mean100.191
Median Absolute Deviation (MAD)13
Skewness-0.020175203
Sum500955
Variance393.08994
MonotonicityNot monotonic
2025-10-13T00:55:03.769414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105115
 
2.3%
97110
 
2.2%
91110
 
2.2%
94106
 
2.1%
103106
 
2.1%
101104
 
2.1%
96100
 
2.0%
104100
 
2.0%
10299
 
2.0%
9899
 
2.0%
Other values (116)3951
79.0%
ValueCountFrequency (%)
01
 
< 0.1%
121
 
< 0.1%
361
 
< 0.1%
371
 
< 0.1%
381
 
< 0.1%
421
 
< 0.1%
431
 
< 0.1%
442
 
< 0.1%
451
 
< 0.1%
465
0.1%
ValueCountFrequency (%)
1701
 
< 0.1%
1691
 
< 0.1%
1681
 
< 0.1%
1641
 
< 0.1%
1591
 
< 0.1%
1571
 
< 0.1%
1561
 
< 0.1%
1555
0.1%
1544
0.1%
1531
 
< 0.1%

eve.charge
Real number (ℝ)

High correlation 

Distinct1659
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.054322
Minimum0
Maximum30.91
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:03.917197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.0695
Q114.14
median17.09
Q319.9
95-th percentile24.112
Maximum30.91
Range30.91
Interquartile range (IQR)5.76

Descriptive statistics

Standard deviation4.2968433
Coefficient of variation (CV)0.2519504
Kurtosis0.051288785
Mean17.054322
Median Absolute Deviation (MAD)2.89
Skewness-0.010990328
Sum85271.61
Variance18.462862
MonotonicityNot monotonic
2025-10-13T00:55:04.065460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.915
 
0.3%
14.2515
 
0.3%
16.1214
 
0.3%
18.7913
 
0.3%
16.9713
 
0.3%
18.9613
 
0.3%
19.4112
 
0.2%
17.0911
 
0.2%
16.811
 
0.2%
18.6211
 
0.2%
Other values (1649)4872
97.4%
ValueCountFrequency (%)
01
< 0.1%
1.91
< 0.1%
2.651
< 0.1%
3.211
< 0.1%
3.541
< 0.1%
3.591
< 0.1%
3.611
< 0.1%
3.731
< 0.1%
4.022
< 0.1%
4.091
< 0.1%
ValueCountFrequency (%)
30.911
< 0.1%
30.751
< 0.1%
30.541
< 0.1%
30.111
< 0.1%
29.931
< 0.1%
29.891
< 0.1%
29.831
< 0.1%
29.791
< 0.1%
29.71
< 0.1%
29.661
< 0.1%

night.mins
Real number (ℝ)

High correlation 

Distinct1853
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.39162
Minimum0
Maximum395
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:04.211671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile117.395
Q1166.9
median200.4
Q3234.7
95-th percentile283.405
Maximum395
Range395
Interquartile range (IQR)67.8

Descriptive statistics

Standard deviation50.527789
Coefficient of variation (CV)0.25214522
Kurtosis0.082359197
Mean200.39162
Median Absolute Deviation (MAD)33.8
Skewness0.019324917
Sum1001958.1
Variance2553.0575
MonotonicityNot monotonic
2025-10-13T00:55:04.369048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188.211
 
0.2%
194.311
 
0.2%
186.211
 
0.2%
214.610
 
0.2%
208.910
 
0.2%
228.110
 
0.2%
2109
 
0.2%
192.79
 
0.2%
193.69
 
0.2%
214.79
 
0.2%
Other values (1843)4901
98.0%
ValueCountFrequency (%)
01
< 0.1%
23.21
< 0.1%
43.71
< 0.1%
451
< 0.1%
46.71
< 0.1%
47.41
< 0.1%
50.12
< 0.1%
50.91
< 0.1%
53.31
< 0.1%
541
< 0.1%
ValueCountFrequency (%)
3951
< 0.1%
381.91
< 0.1%
381.61
< 0.1%
377.51
< 0.1%
367.71
< 0.1%
364.91
< 0.1%
364.31
< 0.1%
359.91
< 0.1%
355.11
< 0.1%
354.91
< 0.1%

night.calls
Real number (ℝ)

Distinct131
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.9192
Minimum0
Maximum175
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:04.523791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile132
Maximum175
Range175
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.958686
Coefficient of variation (CV)0.19974826
Kurtosis0.14443808
Mean99.9192
Median Absolute Deviation (MAD)13
Skewness0.0021328427
Sum499596
Variance398.34914
MonotonicityNot monotonic
2025-10-13T00:55:04.682401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105121
 
2.4%
102109
 
2.2%
100108
 
2.2%
104106
 
2.1%
99105
 
2.1%
103104
 
2.1%
91103
 
2.1%
94103
 
2.1%
95102
 
2.0%
98102
 
2.0%
Other values (121)3937
78.7%
ValueCountFrequency (%)
01
 
< 0.1%
121
 
< 0.1%
331
 
< 0.1%
361
 
< 0.1%
382
< 0.1%
401
 
< 0.1%
411
 
< 0.1%
424
0.1%
431
 
< 0.1%
441
 
< 0.1%
ValueCountFrequency (%)
1751
< 0.1%
1701
< 0.1%
1681
< 0.1%
1661
< 0.1%
1651
< 0.1%
1641
< 0.1%
1611
< 0.1%
1601
< 0.1%
1592
< 0.1%
1582
< 0.1%

night.charge
Real number (ℝ)

High correlation 

Distinct1028
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.017732
Minimum0
Maximum17.77
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:04.826366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.28
Q17.51
median9.02
Q310.56
95-th percentile12.7505
Maximum17.77
Range17.77
Interquartile range (IQR)3.05

Descriptive statistics

Standard deviation2.2737627
Coefficient of variation (CV)0.25214352
Kurtosis0.082377615
Mean9.017732
Median Absolute Deviation (MAD)1.52
Skewness0.019286744
Sum45088.66
Variance5.1699966
MonotonicityNot monotonic
2025-10-13T00:55:04.985398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.6619
 
0.4%
8.4719
 
0.4%
10.818
 
0.4%
9.6318
 
0.4%
8.1518
 
0.4%
9.418
 
0.4%
10.2618
 
0.4%
9.4517
 
0.3%
10.4917
 
0.3%
10.3516
 
0.3%
Other values (1018)4822
96.4%
ValueCountFrequency (%)
01
< 0.1%
1.041
< 0.1%
1.971
< 0.1%
2.031
< 0.1%
2.11
< 0.1%
2.131
< 0.1%
2.252
< 0.1%
2.291
< 0.1%
2.41
< 0.1%
2.431
< 0.1%
ValueCountFrequency (%)
17.771
< 0.1%
17.191
< 0.1%
17.171
< 0.1%
16.991
< 0.1%
16.551
< 0.1%
16.421
< 0.1%
16.391
< 0.1%
16.21
< 0.1%
15.981
< 0.1%
15.971
< 0.1%

customer.calls
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5704
Minimum0
Maximum9
Zeros1023
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-10-13T00:55:05.108327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3063633
Coefficient of variation (CV)0.83186662
Kurtosis1.4810955
Mean1.5704
Median Absolute Deviation (MAD)1
Skewness1.0424623
Sum7852
Variance1.7065852
MonotonicityNot monotonic
2025-10-13T00:55:05.209040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11786
35.7%
21127
22.5%
01023
20.5%
3665
 
13.3%
4252
 
5.0%
596
 
1.9%
634
 
0.7%
713
 
0.3%
92
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
01023
20.5%
11786
35.7%
21127
22.5%
3665
 
13.3%
4252
 
5.0%
596
 
1.9%
634
 
0.7%
713
 
0.3%
82
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
82
 
< 0.1%
713
 
0.3%
634
 
0.7%
596
 
1.9%
4252
 
5.0%
3665
 
13.3%
21127
22.5%
11786
35.7%
01023
20.5%

churn
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4293 
True
707 
ValueCountFrequency (%)
False4293
85.9%
True707
 
14.1%
2025-10-13T00:55:05.348106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-10-13T00:54:57.595231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:28.122628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:29.721955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:31.325043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:33.393641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:36.227543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:37.854277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:39.602470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:41.370372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:43.215394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:45.501779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:48.107961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:50.917979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:53.675769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:55.732248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:57.703125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:28.230785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:29.825106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:31.436900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:33.598499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:36.323360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:37.957675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:39.710298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:41.482190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:43.328807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:45.655509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:48.224819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:51.310720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:53.824823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:55.851657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:57.816632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:28.329328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:29.922938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:31.547155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:33.791011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:36.429452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:38.073119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:39.824002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:41.677697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:43.446352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:45.855162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:48.343642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:51.570144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:53.952245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:55.968064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:57.938939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:28.438675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:30.032604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:31.664193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:33.997568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:36.540663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:38.191882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:39.947598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:41.812698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:43.571681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:46.070389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:48.546671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:51.751070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:54.111331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:56.098303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:58.060110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:28.552876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:30.140165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:31.778564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:34.171493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:36.655456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:38.320204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:40.069533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:41.932001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:43.691143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:46.260206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:48.710988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:51.918191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:54.251329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:56.221080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:58.165969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:28.652436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:30.244402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:31.890532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:34.312814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:36.751464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:38.432084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:40.175787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:42.046102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:43.810989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:46.391458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:48.845717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:52.042721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:54.370861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:56.341348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:58.280292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:28.767302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:30.344662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:32.002684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:34.536827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:36.860031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:38.545592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:40.292342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:42.166324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:43.932025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:46.541686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:49.229358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:52.164441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:54.499597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:56.463568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:58.390840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:28.883129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:30.455040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:32.114409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:34.700579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:36.967160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:38.660635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:40.408506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:42.279152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:44.048307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:47.096317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:49.503587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:52.424352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:54.620442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:56.585202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:58.501819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:28.981817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:30.565253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:32.227399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:35.326248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:37.073271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:38.778947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:40.521792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:42.389923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:44.169135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:47.219954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:49.733688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:52.562398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:54.744803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:56.705212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:58.620887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:29.088816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:30.681287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:32.343184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:35.490374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:37.178886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:38.907259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:40.636786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:42.505373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:44.285975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:47.346054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:49.852052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:52.690970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:54.867938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:56.829232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:58.744892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:29.190040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:30.794831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:32.456561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:35.617995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:37.290542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:39.026970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:40.770835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:42.627447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:44.409793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:47.465324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:50.014849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:52.819933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:54.987792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:56.951278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:58.853437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:29.293669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:30.895107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:32.573792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:35.761261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:37.403898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:39.140338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:40.894852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:42.739513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:44.533869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:47.585114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:50.265404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:53.029545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:55.166073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:57.077375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:58.972610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:29.407075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:31.008785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:32.743424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:35.883705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:37.516196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:39.257242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:41.018625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:42.862116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:44.770484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:47.709097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:50.503598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:53.158808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:55.371652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:57.203740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:59.091904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:29.516791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:31.115060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:33.041007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:36.001282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:37.632020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:39.371718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:41.136773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:42.980401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:45.094768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:47.835228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:50.631923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:53.321164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:55.491833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:57.348429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:59.215552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:29.627200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:31.226861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:33.218744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:36.116618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:37.743469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:39.492369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:41.258692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:43.108514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:45.313315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:47.963780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:50.767355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:53.510288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:55.618288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-13T00:54:57.470853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-13T00:55:05.540477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
account.lengtharea.codechurncustomer.callsday.callsday.chargeday.minseve.callseve.chargeeve.minsintl.callsintl.chargeintl.minsintl.plannight.callsnight.chargenight.minsvoice.messagesvoice.plan
account.length1.0000.0000.000-0.0080.0250.0050.0050.008-0.012-0.0140.0180.0060.0060.000-0.0060.0000.000-0.0100.000
area.code0.0001.0000.0000.0080.0230.0330.0350.0000.0000.0000.0120.0000.0000.0250.0000.0000.0000.0000.000
churn0.0000.0001.0000.3120.0270.3620.3620.0000.0830.0840.0790.0580.0580.2580.0000.0370.0370.1090.109
customer.calls-0.0080.0080.3121.000-0.014-0.001-0.0010.012-0.018-0.015-0.011-0.016-0.0160.022-0.001-0.015-0.015-0.0120.018
day.calls0.0250.0230.027-0.0141.0000.0060.0060.0110.0020.0000.0090.0070.0070.000-0.0040.0020.003-0.0040.000
day.charge0.0050.0330.362-0.0010.0061.0000.9980.008-0.011-0.011-0.006-0.023-0.0230.0390.0040.0030.0030.0100.033
day.mins0.0050.0350.362-0.0010.0060.9981.0000.008-0.011-0.012-0.006-0.023-0.0230.0380.0030.0030.0030.0090.031
eve.calls0.0080.0000.0000.0120.0110.0080.0081.0000.0020.0010.006-0.011-0.0110.000-0.0160.0080.008-0.0020.000
eve.charge-0.0120.0000.083-0.0180.002-0.011-0.0110.0021.0000.9890.0110.0090.0090.0000.014-0.016-0.0160.0240.038
eve.mins-0.0140.0000.084-0.0150.000-0.011-0.0120.0010.9891.0000.0080.0100.0100.0000.019-0.015-0.0150.0220.037
intl.calls0.0180.0120.079-0.0110.009-0.006-0.0060.0060.0110.0081.0000.0060.0060.000-0.000-0.013-0.013-0.0100.000
intl.charge0.0060.0000.058-0.0160.007-0.023-0.023-0.0110.0090.0100.0061.0001.0000.0000.005-0.007-0.0070.0020.000
intl.mins0.0060.0000.058-0.0160.007-0.023-0.023-0.0110.0090.0100.0061.0001.0000.0000.005-0.007-0.0070.0020.000
intl.plan0.0000.0250.2580.0220.0000.0390.0380.0000.0000.0000.0000.0000.0001.0000.0000.0450.0460.0000.000
night.calls-0.0060.0000.000-0.001-0.0040.0040.003-0.0160.0140.019-0.0000.0050.0050.0001.0000.0170.0170.0070.000
night.charge0.0000.0000.037-0.0150.0020.0030.0030.008-0.016-0.015-0.013-0.007-0.0070.0450.0171.0001.0000.0000.010
night.mins0.0000.0000.037-0.0150.0030.0030.0030.008-0.016-0.015-0.013-0.007-0.0070.0460.0171.0001.0000.0000.010
voice.messages-0.0100.0000.109-0.012-0.0040.0100.009-0.0020.0240.022-0.0100.0020.0020.0000.0070.0000.0001.0000.998
voice.plan0.0000.0000.1090.0180.0000.0330.0310.0000.0380.0370.0000.0000.0000.0000.0000.0100.0100.9981.000

Missing values

2025-10-13T00:54:59.403379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-13T00:54:59.610935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

statearea.codeaccount.lengthvoice.planvoice.messagesintl.planintl.minsintl.callsintl.chargeday.minsday.callsday.chargeeve.minseve.callseve.chargenight.minsnight.callsnight.chargecustomer.callschurn
0KSarea_code_415128yes25no10.032.70265.111045.07197.49916.78244.79111.011no
1OHarea_code_415107yes26no13.733.70161.612327.47195.510316.62254.410311.451no
2NJarea_code_415137no0no12.253.29243.411441.38121.211010.30162.61047.320no
3OHarea_code_40884no0yes6.671.78299.47150.9061.9885.26196.9898.862no
4OKarea_code_41575no0yes10.132.73166.711328.34148.312212.61186.91218.413no
5ALarea_code_510118no0yes6.361.70223.49837.98220.610118.75203.91189.180no
6MAarea_code_510121yes24no7.572.03218.28837.09348.510829.62212.61189.573no
7MOarea_code_415147no0yes7.161.92157.07926.69103.1948.76211.8969.530no
8LAarea_code_408117no0no8.742.35184.59731.37351.68029.89215.8909.711no
9WVarea_code_415141yes37yes11.253.02258.68443.96222.011118.87326.49714.690no
statearea.codeaccount.lengthvoice.planvoice.messagesintl.planintl.minsintl.callsintl.chargeday.minsday.callsday.chargeeve.minseve.callseve.chargenight.minsnight.callsnight.chargecustomer.callschurn
4990NDarea_code_510140no0no7.562.03244.711541.60258.610121.98231.311210.411yes
4991AZarea_code_51097no0no8.852.38252.68942.94340.39128.93256.56711.541yes
4992MTarea_code_41583no0no10.362.78188.3700.00243.88820.72213.7799.620no
4993WVarea_code_40873no0no11.563.11177.98930.24131.28211.15186.2898.383no
4994NCarea_code_40875no0no6.971.86170.710129.02193.112616.41129.11045.811no
4995HIarea_code_40850yes40no9.952.67235.712740.07223.012618.96297.511613.392no
4996WVarea_code_415152no0no14.723.97184.29031.31256.87321.83213.61139.613yes
4997DCarea_code_41561no0no13.643.67140.68923.90172.812814.69212.4979.561no
4998DCarea_code_510109no0no8.562.30188.86732.10171.79214.59224.48910.100no
4999VTarea_code_41586yes34no9.3162.51129.410222.00267.110422.70154.81006.970no